Sorry if this is too trivial, but let me start at the "very beginning:" Linear regression. The goal of (ordinary least-squares) linear regression is to find the optimal weights that -- when linearly combined with the inputs -- result in a model that minimizes the vertical offsets between the target and explanatory variables, but let's not get distracted by model fitting, which is a different topic;). So, in linear regression, we compute a linear combination of weights and inputs (let's call this function the "net input function"). Next, let's consider logistic regression. Here, we put the net input z through a non-linear "activation function" -- the logistic sigmoid function where.

There's no single book that can help you fully master machine learning. It's a complicated subject that spans many topics, purposes, and of course benefits in real-world applications. But this post should help novices and experts alike find the right book to continue their education. With so many resources available it can be tough knowing where to start. I'll consider 10 books and look at each one's teaching style, subject matter, and recency of publication.

IBM Research is a Lab and Center member of CRA. This article is the first in a series of our industry member profiles. It's not surprising that the public's imagination has been ignited by artificial intelligence since the term was first coined in 1955. In the ensuing 60 years, we have been alternately captivated by its promise, wary of its potential for abuse, and frustrated by its sometimes slow development. But like so many advanced technologies that were conceived before their time, artificial intelligence has come to be widely misunderstood--co-opted by Hollywood, mischaracterized by the media, and portrayed as everything from savior to scourge of humanity.

Recently popularized by IBM's highly intelligent Watson supercomputer, which competed on the hit game show Jeopardy, cognitive computing refers to machines that are capable of learning concepts and patterns through advanced language processing algorithms. A system that involves incredibly advanced artificial intelligence, cognitive computing is one facet of computer science that isn't for the faint of heart. Although much of the hype around cognitive computing is centered on big business and big data processing, there are a number of consumer applications. Whereas business leaders might use the technology to increase their bottom line, streamline daily operations and achieve greater profitability, consumers can take advantage of cognitive computing to ease some of the burdens of everyday life. In fact, many consumers are using some form of cognitive computing without realizing it.

Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.

The system, known as WaveNet, is able to generate human speech by forming individual sound waves that are used in a human voice. Additionally, because it is designed to mimic human brain function, WaveNet is capable of learning from extremely detailed -- at least 16,000 samples per second -- audio samples. The program statistically chooses which samples to use and pieces them together, producing raw audio. While most of the existing TTS systems also use the same "piece by piece" idea, they largely utilize concatenative TTS. Despite drawing from a large database, these systems are restricted to combinations of short recorded speech fragments from a single speaker, which makes modifying the voice or its inflection difficult.

If you want to talk about something important or sensitive, odds are that conversation will happen over the phone rather than email or text. The sound of someone's voice is an important part of trusting them--but the ability to trust the voice on the other end of that call is human might change. Google DeepMind announced a new speech generation method it calls WaveNet, which could bring artificial intelligence closer than ever to indiscernibly mimicking human speech. The algorithm can easily learn different voices and even generates artificial breaths, according to a DeepMind blog post. DeepMind, the London-based AI firm acquired by Google in 2014, broadly works to "solve intelligence," a goal that spans health, data center energy efficiency, and ancient Chinese board games.

Verifying your identity with brainwave scans sounds pretty awesome on the surface, but new research from from Texas Tech University shows that EEG identification could be the start of a whole new world of privacy concerns. It's not so much that brainwaves are susceptible to forgery, like fingerprints -- it's that EEG scanning applications can glean a lot more from your grey matter than you might want. Security researchers Abdul Serwadda and Richard Matovu recently tested a pair of EEG authentication systems to see if it was possible to identify personal information from anonymous brainwave scans. The pair created a machine learning system that compared the new brainwaves scans to EEG scans from a group of known alcoholics -- and the machine successfully identified 25-percent of persons who identified themselves as alcoholics in the new sample. "We weren't surprised," Serwadda said of the experiment.

An implementation of support vector machines (SVM) is available in the JMSL Numerical Library as of release 7.3. The data mining functionality in JMSL continues to expand with the latest release, including areas such as decision trees and bootstrap aggregation. The documentation of the IMSL Libraries is detailed and robust, but the algorithm discussion and examples can only cover a finite set of use cases. This series of blog posts walks through some additional examples with a focus on classification, starting with the textbook examples part of most SVM resources. Notes and key points are highlighted throughout to provide a complementary resource for users new to SVM or new to using the JMSL Library.

A.I is out to get us. It will replace us all.. or maybe, it's just another tool that we will get used to like calculators or iPhones. If you are reading this post, chances are you are familiar with Artificial Intelligence, Deep Learning, Neural nets. I am not going to be pedantic and use them interchangeably. If you don't care for any of that, feel free to read this article as "Human in Machine Learning loop". Let's start with a practical example of something I built at Rent The Runway.